class_docstr: Class for Tree search based optimisation. 
docstr: cell: tuple 
docstr_tok: ["cell",":","tuple"]
code: def get_value(self, cell, fidel):
      """cell: tuple"""
      # pylint: disable=unused-argument
      self.qcount = self.qcount + 1
      x = np.array([(s[0] + s[1]) / 2.0 for s in list(cell)])
      y = self.doo_obj.eval_single_point_normalised(x)
      if len(self.query_vals) <= self.total_budget:
          self.query_pts.append(x)
          self.query_vals.append(y)
      return y
  
imports: from __future__ import division
  from functools import total_ordering
  from argparse import Namespace
  import numpy
  from .general_utils import map_to_bounds
label_desc: Use image node_id 2 for calling the OptTree obj's underlying member method code with example usage: obj.get_value(cell, fidel) and returns: y